Current breast cancer detection methods often involve mammographic examinations, followed by biopsy procedures. However, mammographies often produce inaccurate results and thereby force many women to undergo unnecessary biopsies, which can be both painful and expensive. To combat the problems related to poor diagnostic accuracy for a number of diseases, research efforts have recently focused on metabolomics to diagnosis diseases through the identification of various biomarkers.1, 2 Metabolomics, which combines high resolution chemical analysis with multivariate statistics, provides a means to identify a subset of metabolites that differentiate sample populations, as well as gives detailed information regarding biochemical status changes.3, 4 A variety of standard analytical instruments have been utilized for metabolomics applications, including Fourier transform infrared (“FTIR”) spectroscopy, nuclear magnetic resonance (“NMR”) spectroscopy, liquid chromatography (“LC”) and gas chromatography coupled mass spectrometry (“GC-MS”).5,6-8,9,10 Moreover, several atmospheric ionization mass spectrometry (“MS”) techniques have also been applied to metabolomics-based studies, including desorption electrospray ionization mass spectrometry (“DESI-MS”),11 extractive electrospray ionization mass spectrometry (“EESI-MS”) and direct analysis in real time (“DART”)12-15 
Among the analytical techniques employed in metabolomics research, NMR has been found to be a useful quantitative and reproducible tool for the analysis of biofluids. NMR has been utilized in a number of studies for detecting diseases and identifying putative biomarkers.15-18 However, because NMR has relatively poor analytical sensitivity, it is often unable to detect species found in low concentrations within the biofluids. As such, in addition to simple 1D 1H-NMR, more advanced NMR approaches, such as two-dimensional (“2D”) J-resolved spectroscopy and selective total correlation spectroscopy (“TOCSY”) experiments, have been explored as alternative analytical techniques within the area of metabolomics research.19-21 
Research has shown that chromatographic methods are ideal compliments to NMR-based metabolomics.13,15,22 In particular, GC-MS has been used for metabolic profiling for over 30 years.23, 24 GC-MS provides a sensitive and reasonably reproducible analytical platform for analysis.25 For example, GC-MS has recently been coupled with multivariate analysis for the differentiation of extracts from Arabidopsis plants to explore the effect of silent plant genotypes.26, 27 However, a major disadvantage of GC-MS is its limited ability to resolve and/or even detect many types of metabolites. This is problematic, particularly because complex biological samples often contain thousands of metabolites.28 Recently, comprehensive two-dimensional GC has been coupled with time-of-flight mass spectrometry (TOF-MS) for the analysis of complex mixtures.29-31 The advantage of two-dimensional gas chromatography (“2D GC” or “GC×GC”) is its ability to add an additional and complementary second separation to the analysis. Coupled with TOF-MS, 2D GC is capable of identifying exact masses of many compounds within complex mixtures. For instance, Mohler and coworkers have used this technique to analyze metabolites in fermenting and respiring yeast.32 Principal component analysis (“PCA”) and parallel factor analysis (“PARAFAC”)33 were applied for the purposes of classification and quantification, respectively. The Synovec group has also developed different algorithms for the analysis of the multi-way data generated from GC×GC-MS including the Fisher ratio method, peak alignment, and the DotMap algorithm.34-36 
The combination of complementary analytical techniques in metabolomics research opens a number of new opportunities.13,15,22 The development of new and emerging technologies in metabolomics has proven to be powerful and promising in a number of cases. For NMR and MS, the combination of two essentially orthogonal analytical techniques, both having extremely high resolving power and the unequivocal ability to identify unknown metabolites, also provides a powerful approach for metabolomics research.
Data from NMR and MS experiments are generally complex since they contain qualitative/quantitative information on upwards of several hundreds of metabolites. Multivariate statistical analyses are thus used for data reduction and in particular for differentiating biofluids samples into “disease” and “control” populations based on the differences in signals of multiple metabolites. A variety of multivariate statistical methodologies provide extremely helpful tools for filtering the large amounts of data and for accessing the often-subtle biochemical perturbations latent in the spectra. In addition, these approaches can be used to extract sets of biomarker metabolites that have the best properties for the assessment of disease status. However, the use of a single analytical method to uncover useful biomarkers for early disease detection, and in particular early breast cancer detection has so far been unsuccessful.
The present invention is intended to address and/or to improve upon one or more of the problems discussed above.